There is a growing expectation that data collected by government-funded studies should be
openly available to ensure research reproducibility, which also increases concerns about …

Statistical agencies have begun to partially synthesize public‐use data for major surveys to
protect the confidentiality of respondents' identities and sensitive attributes by replacing high …

Selecting the number of imputed datasets when using multiple imputation for missing data and
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Objectives. To develop an imputation method to produce estimates for suppressed values
within a shared government administrative data set to facilitate accurate data sharing and …

Objectives Epidemiological studies often have missing data, which are commonly handled
by multiple imputation (MI). Standard (default) MI procedures use simple linear covariate …

Epidemiologic studies are frequently susceptible to missing information. Omitting
observations with missing variables remains a common strategy in epidemiologic studies …

In many contexts, missing data and disclosure control are ubiquitous and challenging
issues. In particular, at statistical agencies, the respondent-level data they collect from …

Background Multiple imputation is becoming increasingly popular for handling missing data.
However, it is often implemented without adequate consideration of whether it offers any …

Background Case-cohort studies are conducted within cohort studies, with the defining
feature that collection of exposure data is limited to a subset of the cohort, leading to a large …

Background Multiple imputation is frequently used to deal with missing data in healthcare
research. Although it is known that the outcome should be included in the imputation model …